--- title: How a Neural Network Learned Its Own Fraud Rules: A... description: A reproducible experiment extending a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during... canonical: https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment | daily.dev og:description: A reproducible experiment extending a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during... og:url: https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn og:image: https://api.daily.dev/og/posts/A4SiIK3DN.png og:image:alt: How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment **[Towards Data Science](https://daily.dev/sources/tds)** · 18 min read · 1 upvotes · 0 comments ## Summary A reproducible experiment extending a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during training — without any hand-coded feature guidance. Using the Kaggle Credit Card Fraud dataset (0.17% fraud rate), the model independently rediscovered V14, a feature long known to correlate strongly with fraud. The architecture combines a standard MLP with a learnable discretizer, a rule learner layer, and temperature annealing to harden soft weights into crisp symbolic rules. The rule learner achieved ROC-AUC 0.933 ± 0.029 with 99.3% fidelity to the neural network's predictions. Key design choices include a three-part loss (detection + consistency + sparsity), a consistency mask that teaches rules to follow the MLP's confident predictions, and L1 regularization to keep rules sparse and readable. Practical deployment considerations cover annealing speed sensitivity, interpretability budget via n_rules, MLP calibration requirements, and the need to re-audit learned rules after every retrain cycle. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules](https://daily.dev/posts/hybrid-neuro-symbolic-fraud-detection-guiding-neural-networks-with-domain-rules-yml9wqde4) · Towards Data Science · 1 upvotes · 0 comments - [Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection](https://daily.dev/posts/explainable-ai-in-production-a-neuro-symbolic-model-for-real-time-fraud-detection-f4v64el7h) · Towards Data Science · 0 upvotes · 0 comments - [Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops \(Label-Free\)](https://daily.dev/posts/neuro-symbolic-fraud-detection-catching-concept-drift-before-f1-drops-label-free--u2bjhafbs) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#pytorch](https://daily.dev/tags/pytorch), [#fraud-detection](https://daily.dev/tags/fraud-detection) [View this post on daily.dev](https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment","url":"https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn"},"datePublished":"2026-03-17T12:55:57.549Z","dateModified":"2026-03-17T12:58:28.368Z","description":"A reproducible experiment extending a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/25511e9c83760f11ed75a3321e6e90b5?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/25511e9c83760f11ed75a3321e6e90b5?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-a-neural-network-learned-its-own-fraud-rules-a-neuro-symbolic-ai-experiment-a4siik3dn","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"deep-learning,pytorch,fraud-detection","timeRequired":"PT18M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment"}]} ```